Reporting on FIA’s Forest-Land Indicators for the Northeastern US
William H. McWilliams, Richard A. Birdsey, Charles J. Barnett, Todd W. Bowersox, Brett J. Butler, Daniel A. Devlin, Patrick Brose, Connie Carpenter, Stephen W. Evans, Linda S. Heath, John L. Hom, Kurt W. Gottschalk, Michael L. Hoppus,
Jennifer C. Jenkins, Kenneth M. Laustsen, Andrew J. Lister, Tonya W. Lister, Barbara M. O’Connell, Brian M. LaPointe, Kevin McCullough, Richard A. McCullough, Manfred Mielke, Peter S. Murdoch, Yude Pan, Gordon C. Reese, Rachel
Riemann, Kim C. Steiner, James R. Steinman, Margaret Weeks, Eric H. Wharton, and Richard H. Widmann.
Abstract: The USDA Forest Service’s Forest Inventory and Analysis (FIA) project contributes to state, regional, national, and international assessments of forest health by providing indicators
from the four-phase measurement system employed across the Northeastern US. Forested ecosystems of the Northeast are strongly influenced by social forces--their extent, composition,
structure, function, and vitality are the result of human impacts. As such, the forces of urbanization, parcelization, and fragmentation are valuable descriptors of the effects of human
populations and associated patterns on the landscape. Trees growing on land technically not classified as forest add important benefits to society. The traditional suite of FIA reports and other
products provide valuable insight into resource issues, for example, the status of balsam fir in Maine following severe spruce budworm outbreaks and salvage harvesting. Some issues, such as
the status of oak, transcend FIA regional boundaries and require larger-scale analysis. New annual inventory reporting protocols include reporting for years prior to full completion of each
State’s inventory and publishing comprehensive analytical reports every five years. The incorporation of forest health monitoring indicators has widened the array of ecological indicators that
can be examined. Integrating intensive site monitoring (ISM) data with FIA samples offers promise in addressing “scaling up” issues. For example, collecting information on carbon
associated with forest litter at ISM sites provides the opportunity to develop a model for predicting this component across the northeast. Adjunct studies and other value-added research fill
gaps between standard reporting products and special issues. The purpose of this poster is to present forest-land indicators being developed and reported for the Northeastern United States.
Examples of other indicators include estimates of carbon budgets, regeneration adequacy, timber products output, and net primary productivity. Most of the indicators discussed fall under
Criteria of the Montreal Process.
Estimation Procedures: There are many options for estimating “ urban forestland.” In the past FIA has used estimates based on
land-use conditions surrounding FIA sample plots. The traditional estimate includes the area represented by all forested sample
plots that are completely surrounded by urban development based on evaluation by FIA field crews. This can be a difficult
variable to classify in the field, so alternative methods have been explored with the intent of developing a method that is
objective, repeatable, and available for use across the northeastern US. A study in NJ used the 1990 Bureau of Census
population density and urban/rural/mixed classes to classify FIA’s Phase 1 and 2 samples. Four approaches were evaluated:
1) classify and sum FIA P1 plots with population density > 1000 per square mile,
2) classify and sum FIA P1 plots classified as “ census urban (see first map at right),”
3) classify and sum FIA P2 plots with population density > 1000 per square mile,
4) classify and sum FIA P2 plots classified as “ census urban” (see second map at right).
The study concluded that classifying FIA P2 plots using either the 1000 per square mile threshold or the census urban definition
produced meaningful estimates of urban forestland and that the use of Block Group level data are recommended. The use of the
population threshold may be preferred because it avoids the issue of the “ mixed” designation. T he chart to the right displays the
results for northeastern states. Future work will address what type of forest was captured or missed in each category and how
the resulting definition fits with contemporary use and understanding of the term “ urban forest.”
Urban Forestland
State
Urbanization: extent and abundance of people in the context
of forest location and function is key to understanding how
“ urban” forest is defined. Forests that meet standard FIA
definitions must be at least an acre in size (or 120 feet wide)
and have an understory that provides the opportunity for
renewal (regeneration). By relating population statistics
developed by the U.S. Bureau of Census to FIA locations that
meet these requirements, estimates of forestland for different
population levels reveals interesting information on “forests in
an urban setting.” T his supplements more intensive inventories
conducted within urban areas for street trees, clumps of trees,
and other arboreal assemblages (see Trees on Non-Forest Land
below).
Northeastern FIA Region
DE
VT
RI
WV
NH
ME
CT
OH
MD
NJ
PA
NY
MA
0
10 0
2 00
300
400
500
600
70 0
Thousands of Acres
Parcelization: an increasingly common trend in our Nation’s
forests is the subdivision of larger tracts owned by a single
ownership into multiple smaller tracts owned by many or at
least more ownerships. These subdivisions can be invisible on
the landscape, but can also be the precursor of significant
changes. If the forest structure remains intact, the ecological
processes will continue, but access to the resource and
management goals can change significantly when
parcelization results in land-use changes, such as clearing land
for a home site, ecological processes will be altered, and
fragmentation can ensue.
Estimation Procedures: Information on
parcelization comes from the National
Woodland Owner Study (NWOS). The
NWOS provides timely information on
the number of owners and area owned
for private woodland owners across the
country. The NWOS samples private
forestland owners. Questions relating to
parcel size are used to quantify the
results by state and inventory region.
Information on trends in parcelization
come from successive owner surveys.
Example: Parcelization data for Pennsylvania
are shown in the table at the right. Comparing
regions and survey dates reveals some
interesting trends. For example, comparing the
percentage of private acres in the largest parcels
(1000+ acres) indicates that the industrial forests
of the Allegheny Region had 22 percent of
private forest in large parcels, compared to only
5 percent in the urbanized Southeastern Region.
However, the Allegheny Region is experiencing
parcelization, as the area in large parcels
decreased by more than one half.
Forest Parcelization in Pennsylvania
For mo re information, please consult the NWOS web pag e:
Estima ted number of private ownerships and private acres of forest-land by size class and region, 1993, 1978, and difference
www. fs.fe d.us/ woo dlan do wners/
1993
Pa rce l
Si ze
1-99
10 0-19 9
20 0-49 9
50 0-99 9
10 00 +
Weste rn
Own ers Acres
1 30 ,5 00 1 ,6 88 ,90 0
2 ,8 00 34 8, 70 0
7 00 17 4, 30 0
1 00 6 5, 40 0
1 00 19 5, 30 0
1 34 ,2 00 2 ,4 72 ,60 0
Alle gh eny
Owners Acres
43 ,7 00 7 19 ,60 0
2 ,1 00 3 11 ,80 0
9 00 2 75 ,80 0
2 00 1 32 ,70 0
1 00 4 03 ,00 0
47 ,0 00 1 ,8 42 ,90 0
No rt h Cen tral
Owners Acres
33 ,4 00 6 78 ,10 0
1 ,9 00 2 42 ,20 0
1 ,5 00 4 11 ,70 0
3 00 1 81 ,60 0
2 00 4 70 ,70 0
37 ,3 00 1 ,9 84 ,30 0
Sou th west ern
Owners Acres
3 4,0 00 5 38 ,20 0
2,1 00 2 79 ,90 0
7 00 2 36 ,80 0
1 00
96 ,90 0
1 00 2 08 ,30 0
3 7,0 00 1,3 60 ,1 00
No rthe astern
Owners Ac res
5 0,7 00 7 18 ,60 0
2,2 00 2 87 ,40 0
6 00 1 64 ,20 0
1 00
30 ,80 0
W 88 ,60 0
5 3,6 00 1,2 89 ,6 00
Poc on o
Owners Ac res
5 1,1 00 5 49 ,8 00
1,1 00 1 39 ,9 00
6 00 1 69 ,9 00
2 00 1 10 ,0 00
1 00 3 91 ,4 00
5 3,1 00 1,3 61 ,0 00
So ut hea st ern
Owners Acres
8 1,6 0 0 70 6,7 00
6 00 7 9,4 00
3 00 7 9,4 00
W 2 3,8 00
W 4 7,6 00
8 2,5 0 0 93 6,9 00
So uth Ce ntral
Own ers Acres
67 ,20 0 71 9,2 00
1 ,40 0 17 9,8 00
60 0 17 1,2 00
10 0 4 2,8 00
10 0 13 4,5 00
69 ,40 0 1 ,24 7, 500
Sta te
Own ers Acres
4 92 ,20 0 6 ,3 19 ,10 0
14 ,20 0 1 ,8 69 ,10 0
5 ,90 0 1 ,6 83 ,30 0
1 ,10 0 6 84 ,00 0
70 0 1 ,9 39 ,40 0
5 14 ,10 0 1 2,4 94 ,9 00
Weste rn
Own ers Acres
1 18 ,0 00 1 ,6 49 ,10 0
3 ,4 00 39 9, 50 0
7 00 17 8, 90 0
1 00 5 1, 80 0
W 14 6, 20 0
1 22 ,2 00 2 ,4 25 ,50 0
Alle gh eny
Owners Acres
32 ,7 00 5 80 ,40 0
1 ,3 00 1 75 ,40 0
9 00 2 15 ,00 0
1 00
87 ,70 0
1 00 8 59 ,60 0
35 ,1 00 1 ,9 18 ,10 0
No rt h Cen tral
Owners Acres
50 ,8 00 7 89 ,50 0
2 ,5 00 3 23 ,50 0
9 00 2 74 ,60 0
2 00 1 19 ,20 0
2 00 5 81 ,10 0
54 ,6 00 2 ,0 87 ,90 0
Sou th west ern
Owners Acres
4 7,1 00 5 80 ,20 0
2,2 00 2 94 ,90 0
7 00 1 90 ,60 0
1 00
57 ,10 0
1 00 2 06 ,50 0
5 0,2 00 1,3 29 ,3 00
No rthe astern
Owners Ac res
4 8,7 00 6 00 ,20 0
2,2 00 2 63 ,80 0
6 00 2 00 ,80 0
1 00
55 ,10 0
W 1 18 ,10 0
5 1,6 00 1,2 38 ,0 00
Poc on o
Owners Ac res
3 5,4 00 5 31 ,6 00
1,7 00 2 28 ,2 00
6 00 1 87 ,4 00
2 00 1 24 ,9 00
1 00 3 25 ,5 00
3 8,0 00 1,3 97 ,6 00
So ut hea st ern
Owners Acres
7 9,8 0 0 69 7,7 00
5 00 7 2,7 00
1 00 1 6,5 00
W 2,0 00
W 7 0,3 00
8 0,4 0 0 85 9,2 00
So uth Ce ntral
Own ers Acres
55 ,60 0 62 1,2 00
1 ,60 0 21 0,1 00
70 0 18 4,8 00
W 3 1,5 00
10 0 14 9,6 00
58 ,00 0 1 ,19 7, 200
Sta te
Own ers Acres
4 68 ,10 0 6 ,0 49 ,90 0
15 ,40 0 1 ,9 68 ,10 0
5 ,20 0 1 ,4 48 ,60 0
80 0 5 29 ,30 0
60 0 2 ,4 56 ,90 0
4 90 ,10 0 1 2,4 52 ,8 00
Weste rn
Own ers Acres
12 ,5 00 3 9, 80 0
-6 00 -5 0, 80 0
0
-4, 60 0
0 1 3, 60 0
NA 4 9, 10 0
12 ,0 00 4 7, 10 0
Alle gh eny
Owners Acres
11 ,0 00 1 39 ,20 0
8 00 1 36 ,40 0
0
60 ,80 0
1 00
45 ,00 0
0 -4 56 ,60 0
11 ,9 00 -75 ,20 0
No rt h
Owners
-17 ,4 00
-6 00
6 00
1 00
0
-17 ,3 00
Sou th west ern
Owners Acres
-1 3,1 00 -42 ,00 0
-1 00 -15 ,00 0
0
46 ,20 0
0
39 ,80 0
0
1 ,80 0
-1 3,2 00
30 ,80 0
No rthe astern
Owners Ac res
2,0 00 1 18 ,40 0
0
23 ,60 0
0 -36 ,60 0
0 -24 ,30 0
NA -29 ,50 0
2,0 00
51 ,60 0
Poc on o
Owners Ac res
1 5,7 00
18 ,20 0
-6 00 -88 ,30 0
0 -17 ,50 0
0 -14 ,90 0
0
65 ,90 0
1 5,1 00 -36 ,60 0
So ut hea st ern
Owners Acres
1,8 00
9,0 00
1 00
6,7 00
2 00 6 2,9 00
NA 2 1,8 00
NA -2 2,7 00
2,1 00 7 7,7 00
So uth Ce ntral
Own ers Acres
11 ,60 0 9 8,0 00
-20 0 -3 0,3 00
-10 0 -1 3,6 00
NA 1 1,3 00
0 -1 5,1 00
11 ,40 0 5 0,3 00
Sta te
Own ers Acres
24 ,10 0 2 69 ,20 0
-1 ,20 0
-99 ,00 0
70 0 2 34 ,70 0
30 0 1 54 ,70 0
10 0 -5 17 ,50 0
24 ,00 0
42 ,10 0
1978
Pa rce l
Si ze
1-99
10 0-19 9
20 0-49 9
50 0-99 9
10 00 +
Difference
Pa rce l
Si ze
1-99
10 0-19 9
20 0-49 9
50 0-99 9
10 00 +
Fragmentation: once blocks of forestland are cleared for land
uses other than forestry, the process of forest fragmentation
begins. Fragmentation creates a mosaic of forest patches of
varying size and is perhaps the most direct visual image we
have of forest loss. Forest patches vary in quality with respect
to watershed protection, carbon sequestration, wildlife habitat,
species diversity, timber production, aesthetic values, recreation
opportunities and other amenities. The context of this loss, its
size, and resulting ecological impacts are significant, but little
understood. Statistical techniques for assessing fragmentation
are evolving. One glaring need is for adequate measures of
change in fragmentation over time.
Tre es on Non-Forest Land: FIA’s definition of forestland
encompasses tracts at least 1-acre in size (or at least 120 feet
wide) that have functioning understories (functional in the
context of ubiquitous conditions). Land with trees that do not
meet this definition—there is a wide range of conditions that
could occur, from a wooded picnic ground to a city street with
landscaped trees-- require monitoring for several reasons,
including to estimate contributions to carbon budgets,
characterize floral composition and structure, estimate the
contribution to air and water pollution amelioration, evaluate
worth to human residents, provide baseline statistics on overall
forest health in the larger forested landscape. Often, invasive
plants and other unique pest occur at this important urban-forest
interface.
Estimation Procedures: There are
various useful data sources for
quantifying forest fragmentation,
ranging from aerial photos for finer scale
studies, to classified satellite imagery for
regional assessments. Satellite data can
be obtained in a more cost effective
manner than can aerial photos, but a
great deal of spatial and informational
resolution is lost when using classified
pixels. NE-FIA plans to use a
combination of both data sources in
order to meet its research and analysis
objectives.
Study Region: A six-county region
surrounding Baltimore, MD was chosen to
study the character of tree cover on nonforestland (see map). The Baltimore
study region provides an excellent forestto-urban continuum for studying a variety
of non-forestland conditions. The
diversity of non-forestland conditions is
shown in the three aerial photo’s to the
right.
Example: Fragmentation statistics provide valuable data to
supplement FIA’s traditional estimates of land area by land use. T o
illustrate the need for fragmentation information, the two adjacent
maps illustrate how two areas with nearly identical amounts of
forest can have very different spatial characteristics. The example
on the far right is a prototype of a statistical summary of various
fragmentation statistics calculated for Worcester County in
Massachusetts. Note the combination of tabular, graphical, and map
display of fragmentation information. The intent to make the
information accessible and appealing to NE-FIA’s diverse clientele.
These two scenes show areas of roughly equal percent forest (61 and
62%), but different spatial distributions of forest, and different
surrounding land uses (agriculture vs. residential). We can summarize
the length of boundary between forest and other land uses, average patch
size, average core area size, and various other fragmentation information
to obtain a more clear understanding of the forests in these two areas.
Results: The results provide interesting new information and comparative statistics for use in evaluating the underlying
differences between trees on forestland and trees on non-forestland. T he forested plots tended to have many more trees and
higher basal areas per acre than non-forestland plots. Trees on non-forested plots were fewer in number, but larger in size.
The two types of plots had many tree species in common, but the non-forestland plots added 16 species to the mix for the
study region. Sixty-three percent of the trees on non-forestland were exotic, compared to 21 percent for forested plots.
Information on land use indicates that while most of the non-forestland plots occurred on agricultural land, most of the trees
occurred in residential areas (see box below) T he data also provided an opportunity to add missing information to the region’s
carbon budget and allows a more complete estimate of Net Primary Productivity (see box at right).
Methods: A grid of 1/10 th -acre plots
coincident with the FIA grid was laid
across the region. At each plot, a selected
set of P2 and P3 tree- and condition-level
variables were collected. Data were
summarized to fill in gaps in our
knowledge of the contribution and
character of trees on non-forestland.
Residential
comm/ indus
urban open
Transport
crops/pasture
Open
%NF
41
6
8
6
36
3
Effect on Regional Calculations of Carbon and NPP:
Tree biomass stocks for nonforest land were,
per unit area, roughly 25% of the biomass
computed for forest land.
Wood production (WNPP) for nonforest land
was approximately 22% of that for forest
land.
Calculating this for the entire northeast using
known %’s of F and NF area Æ NF land adds
13% to the total biomass and 24% to the total
WNPP. (assuming ratios of NF:F biomass
and NPP are the same…) -- source: Jenkins
Lan d Use
all NF plots
Presented at the Forest Health Monitoring Workshop, New Orleans, LA, February 4-7 2001
Cen tral
Acres
-1 11 ,40 0
-81 ,30 0
1 37 ,10 0
62 ,40 0
-1 10 ,40 0
-1 03 ,60 0
%NF w/trees
72
2
12
2
7
5
FIA Factoid? Where does the w ord “survey” in the old FIA moniker
(“Forest Survey”) originate from?
NF w/trees
Answer: The term “survey” is of French origin, w ith the common prefix
“sur” indicating “above,” and the suffix “vey” meaning “to see,” that is, “to
see as if from above.”
P1
Focusing on Reporting
Annual
Annual Reporting
Reporting is
is Underway
Underway
in
in Maine
Maine and
and Pennsylvania
Pennsylvania
Ramping-Up in the Early Years
Closing-Out
Closing-Out Periodic
Periodic States
States
NE-FIA is in the process of working
with state partners to produce issueoriented resource reports, such as the
brochure and color booklet to the right.
Other outlets include web pages, fact
sheets, conferences, and workshops.
The remaining states with periodic
inventories yet to be closed out include
Vermont, New Hampshire, Connecticut,
Massachusetts, Rhode Island, New
Jersey, Delaware, Maryland, and West
Virginia. The intention is to complete
the analysis of these states in a timely
fashion and eliminate the backlog that
has developed for completing state-level
analyses.
Visit our web site: www.state.me.us/doc/mfs/mfshome.htm
A
DR
Under the auspices of the national FIA program and the
supervision by state partners, NE-FIA is forging ahead with
reporting for each state following each year’s inventory for the
first five or seven years until the first set of inventory panels
are co mplete. The detail of reporting varies depending on the
number of sample plots measured, variability of forested
landscapes and associated disturbances, and interest from state
partners. At the right, the first annual report in the
northeastern region is shown. Pennsylvania has gained some
insights from a set of tables produced in year one and is
anxiously awaiting the extra level of detail that the second
year’s measure ments will add.
FT
Visit our web site: www.na.fs.fed.us/sustainability
Criteria and Indicators of Sustainability – Montreal Process
(FIA plays a critical role in 1, 2, 3, 5, & 6 and provides supporting data for 4 and 7)
The Comprehensive Analysis
The national Analysis Band is designing a template for
analyzing forested ecosystems across the US. A set of tables,
charts, maps, and other products are being designing for
regional imple mentation in the 5-year comprehensive
analytical reports required under the annual inventory system.
RO
Species = Red Oak
Examples
Examples of
of Issue-Based
Issue-Based Analyses
Analyses
Bole Lengt h = 52 feet
Visit the AB web site: socrates.l v-hrc.ne vad a.edu/fia/ ab
Cubic-foot Cu ll = 6%
9 Criterio n 1:
9 Criterio n 2:
9 Criterio n 3:
Co nserv ation of biol ogic al div ersity.
Ma intena nce o f pr od uctiv e capaci ty o f forest ec os yste ms.
Ma intena nce o f forest ec os yste m he alth an d v itality.
Criterio n 4: Co nserv ation an d mai nte nance of soil a nd wa ter resources .
9 Criterio n 5:
9 Criterio n 6:
Ma intena nce o f forest c on trib uti on to glo bal carb on c ycles.
Ma intena nce an d en han ceme nt of l on g-term mul tiple
soci o-ec on omi c be nefi ts to mee t the ne eds of s ocieties .
Criterio n 7: Le gal ins tituti ona l an d eco no mic fra me work for forest
co nserv ation a nd s ustaina ble ma nage me nt.
Percent Sou nd ness = 9 ( 90- 100% )
DBH = 15.2 i nches
Traditional Forestland Indicators from
Phase 2
NE-FIA has been reporting on standard forestland
indicators since the 1950’s. These indicators provide
valuable trend information on extent, character,
composition, structure, sustainability, and health of
northeastern forests. Although most of the past
information has focused on “timberland” and
“growing-stock tree,” newer reports cover all forestland
and trees. The information has traditionally been
reported in statistical reports, analytical reports, and
other outlets. The basic set of indicators offers an
general picture of forestland conditions that can be
elaborated on as specific forest issues arise. A list of
some of the more commonly used indicators follows:
Distribution of Mi xed-Oak Stands in the Eastern US
How are Composition and Structure
Changing?
Mi xed-Oak b y Stand-Si ze Class
To
Tofurther
furtherexamine
examinethe
theissue,
issue,aakriged
krigedmap
mapof
ofoak
oakspecies
speciesoccurrence
occurrencewas
wasdeveloped.
developed.
The
Thefirst
firstmap
maptotothe
theright
rightshows
showsthe
thedistribution
distributionof
oftimberland
timberlandwith
withatatleast
least25
25percent
percent
basal
basalarea
areain
inoak
oakspecies.
species. The
Theoccurrence
occurrenceof
ofoak
oakininforest
foreststands
standsthroughout
throughoutthe
theeastern
eastern
US
USisisreadily
readilyapparent.
apparent. The
Thelargest
largestblock
blockof
ofhigh
highdensity
densityoak
oakisislocated
locatedininthe
theOzark
Ozark
Plateau
of
Missouri
and
northern
Arkansas.
Other
areas
with
high
oak
density
Plateau of Missouri and northern Arkansas. Other areas with high oak densityare
arethe
the
Central
Centrallowlands
lowlandsof
ofMinnesota
Minnesotaand
andWisconsin,
Wisconsin,the
theAppalachian
AppalachianMountains
Mountainsfrom
fromcentral
central
Pennsylvania
Pennsylvaniatotonorthern
northernAlabama,
Alabama,the
theNashville
NashvilleBasin
Basinof
ofTennessee,
Tennessee,and
andthe
theWestern
Western
Florida
Peninsula.
Of
course,
our
maps
should
be
considered
spatial
bar
charts
that
Florida Peninsula. Of course, our maps should be considered spatial bar charts that
reflect
reflectmajor
majorpatterns
patternsacross
acrossthe
theforested
forestedlandscape--ie,
landscape--ie,species
speciesthat
thatoccur
occursparsely,
sparsely,
such
suchas
asoak
oakin
inVermont,
Vermont,are
arebetter
betteraddressed
addressedby
bystudies
studiessuch
suchas
asLittle’s
Little’s(1975)
(1975)and
and
others’.
others’.To
Toexamine
examinethe
theoccurrence
occurrenceof
ofoak-dominated
oak-dominatedstands,
stands,aamap
mapwas
wasdeveloped
developed
showing
the
distribution
of
stand
sizes
for
stands
where
oak
comprised
at
least
showing the distribution of stand sizes for stands where oak comprised at least
50
percent
of
the
total
basal
area
per
acre.
It
is
immediately
apparent
that
young
50 percent of the total basal area per acre. It is immediately apparent that young
successional
successionalstands
standsare
arerare
rareand
andthat
thatsawtimber-size
sawtimber-sizestands
standspredominate
predominateacross
acrossmost
most
of
ofoak’s
oak’snative
nativerange
rangein
inthe
theEast.
East.
The
Themixed-oak
mixed-oakforests
forestsof
ofPennsylvania
Pennsylvaniahave
havebeen
beenmaturing
maturing
in
inrecent
recentdecades
decadesand
andinventory
inventoryresults
resultsindicate
indicatethat
thatthe
thearea
area
of
ofyoung
youngstands
standsof
ofoak
oakare
aredecreasing.
decreasing. This
Thisisisindicative
indicativeof
ofaa
large-scale
large-scalephenomena
phenomenathat
thatisisoccurring
occurringthroughout
throughoutmuch
much
Of
Ofthe
theeastern
easternUS.
US.
Saw timber
1989
1978
Po le timber
Sapling-Seed ling
0
1000
2000
3000
4000
5000
Tho usand Acres
Area*
Land-use
Ownership class
Forest-type group
Stand-size class
Stocking class
Volume class
Disturbance Class
What is the Status of Tree Mortality?
? Sustainability ?
Numbers of Trees*
The
Theissue
issueof
ofheavy
heavymortality
mortalityhas
hasmajor
majorramifications
ramificationsfor
forthe
thehealth
healthand
andsustainability
sustainabilityof
of
spruce-fir
spruce-firforests
forestsininMaine.
Maine. Species
Speciesdistribution
distributionmaps
mapsprovide
providecontext
contextand
andperspective
perspective
for
forthe
theextent
extentand
andimpact
impactof
ofspruce
sprucebudworm
budwormimpacts.
impacts. The
Themaps
mapsbelow
belowshow
showthe
the
distributions
for
the
eight
of
the
species
shown
in
the
chart.
Relatively
rare
species,
distributions for the eight of the species shown in the chart. Relatively rare species,
such
suchas
asWhite
WhiteSpruce
Spruceare
aretoo
toodifficult
difficulttotomap
mapininthis
thismanner.
manner. To
Tofurther
furtherexamine
examinethe
the
magnitude
magnitudeofofmortality
mortalityon
onBalsam
BalsamFir,
Fir,aamap
mapshowing
showingthe
themortality
mortalitywas
wasdeveloped.
developed.
Because
Becausemortality
mortalityisishighly
highlyvariable,
variable,aamap
mapshowing
showingthe
theassociated
associatedvariability
variabilityofofthe
the
estimate
estimateisisincluded.
included.
During
Duringthe
the1980’s,
1980’s,severe
severespruce
sprucebudworm
budwormoutbreaks
outbreaksravaged
ravaged
the
thespruce-fir
spruce-firforests
forestsof
ofMaine.
Maine. Subsequent
Subsequentmortality
mortalitywas
washeavy,
heavy,
especially
especiallyfor
forbalsam
balsamfir—a
fir—afavorite
favoritehost
hostfor
forthe
thebudworm.
budworm. The
The
chart
below
shows
an
index
of
mortality
for
the
top
ten
species
chart below shows an index of mortality for the top ten species
in
Maine
over
the
period
from
1982
to
1994.
The
heavy
mortality
in Maine over the period from 1982 to 1994. The heavy mortality
of
ofbalsam
balsamfir
firbears
bearscloser
closermonitoring.
monitoring.
Balsam Fir
Balsam Fir
Beech
Aspen
Species
Diameter class
Tree class
P aper Birch
Black S pruce
Red S pruce
Average fo r
All Species
White S pruce
Northern W hite-Cedar
Yell ow Birch
Red M aple
Biomass/Volume*
Variability in the Spatial Dimension: One advantage
of the technique used is that it provides the opportunity to
view an estimate of the variance in a spatial dimension
— thus allowing users to adjust Information with issueoriented insights. See below for the error surface
associated with the balsam fir mortality map.
Paper Birch
0
0.01
0.02
Aspen
Ame rican Bee ch
Yellow Birch
Northern White
Cedar
0.03
Percent
Forest-type group
Stand-size class
Species
Diameter class
Tree class
Tree grade
Legend
> 50% ba/acre
Growth, Removals, and Mortality*
> 20% and < 50%
> 5% and < 20%
Species
Change component
Land-use
Red Maple
Red Spruce
< 5%
0 % or nonforest
water
WP
Disturbance
Disturbance––Human
Humanand
andOther
Other
A Look at Harvesting.. Coming Soon..
P2
Example
Example of
of an
an Adjunct
Adjunct Inventory
Inventory
Pennsylvania Regeneration Study
Design
Study Objectives:
Specific research questions to be answ ered are:
Forest
Forest Health
Health Reports
Reports
Background: Past studies have
suggested that advance
regeneration is often lacking in
forests across Pennsylvania and
that oak regeneration is especially
rare. Beginning w ith the 2001 field
season, the Bureau of Forestry is
conducting a study in cooperation
with FIA. All “established”
seedlings are being measured
during the summer leaf-on season.
With over half of the State’s
timberland in financially mature
saw timber stands, the regeneration
issues have moved to the forefront
of discussion w ithin the
environmental community.
1.
2.
3.
4.
What are the abundance, composition, and quality of advance
regeneration?
What are the abundance, composition, and quality of
regeneration follow ing disturbance?
What are the extent and composition of other understory
vegetation?
What is the status of regeneration of oak and other key
species?
Once the first five-year cycle of inventory plots is completed, the
regeneration data study data w ill begin to address these
questions w ith statistical confidence and provide longer-term
monitoring of regeneration.
Previous Results
All Woody – Advance
40%
60%
Brochures summarizing FHM plot conditions for each of 12 northeastern states are
currently being printed.
Coming Soon..
Each brochure summarizes the conditions on the plots based on the most recent
visit from 1996 through 1999. In addition to plot-level characteristics, crown
conditions for major species groups are summarized and discussed.
In 2002, Northeastern Area
State and Private Forestry will
publish a report summarizing
forest health conditions
throughout the 20 state region.
Similar brochures w ill be developed for the FHM and Phase 3 data collected in the
2000 and 2001 field seasons.
Mixed Oak – Post - Disturbance
16%
Methods: An interpenetrating sub-panel of each year’s annual measurement panel for
the State will be measured during the leaf-on summer window from June through
August. Each FIA sample location is comprised of four 24-foot radius subplots. Each
subplot includes a 6.8-foot radius microplot. At each microplot, all established seedlings
at least 2-inches in height are tallied by seedling source and height class. To be
considered “established,” seedlings must pass the “tug test” or in the case of oaks and
some other species, root collar diameter (RCD) in excess of 0.20 inches in diameter
determines if the seedling is established. A code for “competitive” oak seedlings (RCD
>= 0.75 inches) was also used. Competing vegetation is tallied on the larger subplot by
life form and cover percent.
The report will summarize forest
health conditions from 1996
through 2000. It will include
information from state surveys,
aerial surveys, and the FHM
plot network.
84%
-- Successful
For pe riodic updates on the progress of the study, visit our
web site, as well as those of cooperators at the USFS
Silviculture Labs in Warren, PA and Mo rgantown, WV:
(www.fs.fed.us/ne/ ).
Look for similar reports to be
produced for the foreseeable
future.
Tree
Tree Vitality
This analysis summarizes data collected in the northeastern and mid-Atlantic states betw een 1991 and
1999. Based on w ork done by Steinman, it compares the condition of trees at the most recent visit w ith their
condition at the most recent previous visit. The time betw een visits ranged betw een 1 and 4 years w ith an
average of about 2 years between measurements.
Annual Reporting Update
The national FIA Program is in the fourth year of activities to design, develop, and implement the annual inventory system. The
activities center around “bands” representing the major issues facing FIA during the implementation years. The Analysis and the
Indicator Bands are currently w orking on reporting templates for annual reporting and the five-year comprehensive reports. The
Indicator Advisors are providing templates for core presentation of their respective data to round out the suite of indicators
reported for by FIA. Tree damage is a key element of vitality assessments. The tabular information show n below is a prospective
core table that w ill provide consistent tree-damage information across the US.
In general, trees w ith crown dieback of more than 25%, crow n density of less than 30%, and any stem or
root damage had higher rates of mortality than trees w ith other combinations of conditions. The basic
concept of rating trees and plots is illustrated at the right.
Crow n Dieback
>25%
Table #. Volume of trees by damage agent.
Species
.
.
.
Crow n Density
<30%
No
(n = 8,239)
Causal Agent Æ. . .00 01 02 03 04 05 11 12 13 20 21 22 23 24 25 31 Total
No
(n = 8,556)
Causal Agent Codes:
00
01
02
03
04
05
11
12
13
20
21
22
23
24
25
31
Healthy
Canker, gall
Conks, fruiting bodies, and signs of advanced decay
Open wounds
Resinosis or gummosis
Cracks and seams
Broken bole or roots (< 3-feet f rom bole)
Brooms on roots or bole
Broken or dead roots (> 3-feet from bole)
Vines in crown
Loss of apical dominance, dead terminal
Broken or dead branches
Excessive branching or brooms
Damaged foliage, buds, or shoots
Discoloration of foliage
Other
P3 Data Show Preceding Conditions
Yes
(n = 317)
No
(n = 99)
Yes
(n = 337)
Yes
(n = 238)
Stem/Root Damage
Present
Trees Dead at Most
Recent Visit
No
(n = 5,580)
4%
Yes
(n =2,659)
7%
No
(n = 158)
34%
Yes
(n = 159)
38%
No
(n = 41)
34%
Healthy
No
(n = 108)
69%
Yes
(n = 130)
75%
Fair
Poor
Ratings for Plots
Go od
40%
Dead
Ratings for Trees
Good
Yes
(n = 58)
Sick
Fair
P oor
P3
Integrating
Integrating Intensive
Intensive Site
Site Monitoring
Monitoring and
and FIA
FIA Samples
Samples
A
A Few
Few Examples
Examples of
of Value-Added
Value-Added Indicators
Indicators
Accounting for Carbon in the US
A Framework for Integrating Environmental Monitoring and Related Research in the
Delaware River Basin
Objective: To address ecosystem-level issues through testing of potential national-scale collaborative strategies among existing biological, terrestrial,
aquatic, and atmospheric monitoring and research programs.
ATMOSPHERE
Pre Integration Issue Assessments
Multi-Tier Design
1.Measuring and monitoring forest carbon stocks and
fluxes
2.Identification and monitoring of forests vulnerable to
non-native pest species
3.Monitoring recovery from calcium depletion and nitrogen
saturation in forests of the Appalachian Plateau
4.Monitoring the status and impacts of forest
fragmentation
5.Integrating the effect of terrestrial ecosystem health and
land use on the hydrology, habitat, and w ater quality
of the Delaw are River Basin.
Tier One: Remote sensing and mapping
landscape characteristic
Selection of sampling locations
Tier Tw o: Regional Surveys
Forest characterization (FIA)
Water quality sampling ( USGS)
Tier Three: Intensive Areas
Co-located forest, w ater, and soil process studies
Linkage to surveys
Monitoring at Multiple Scales to Link Processes and Observations
Anticipated Products – Forest Track
Pre-integration issues assessment
Intensive Monitoring sites:
1.
2.
3.
Carbon Estimation: Interest in strategies to
ameliorate the effects of increased carbon dioxide in
the atmosphere has provided yet another extension for
FIA data. Studies of carbon storage and accumulation
in terrestrial ecosystems have drawn heavily on FIA
data to quantify the contribution of forest vegetation.
Estimation procedures have yielded information the
distribution of stored carbon and flux in the United
States (See figure and map). A forest carbon budget
model (FORCARB) has estimated prospective trends
in carbon dynamics. Experience with modeling carbon
has inferred additional information needs for broadscale inventories. Inventory designs have been
enhanced by P3 measures vital for understanding
deeper ecological relationships between plants, soils,
and the environment.
Regional assessment of calcium
depletion, acidification, and nutrient
release
Neversink River Basin
Delaw are Water Gap
French Creek State Park
decay
decay
HARVESTED
CARBON
processing
Recycling
decay
Growth
BIOMASS
Above and Below
Removals
Mortality
Harvest
residue
Litterfall,
Mortality
burning
LITTER
LAYER
PRODUCTS
disposal
LANDFILLS
burning
DOWN
DEAD
WOOD
Humification
ENERGY
burning
Decomposition
Transfers
to other
sectors
SOIL
Imports/
Exports
Managed forest lands, US, 2008-2012
Avg. annual C stock change (Tg/yr)
C taken up by trees in managed forests
381.9
C released by harvesting trees
-276.0
Net C taken up in Soil
52.4
Net C taken up in Floor
12.8
Net C taken up in Understory
0.7
Net C accrued in live biomass & soil
Regional assessment of carbon flux
and the physical controls on invasive
species
STANDING
DEAD
Treefall
Tree Carbon Per Hectare by U.S. County
Regional assessment of forest
fragmentation, land-use change, and
water quality effects
171.8
C increase in logging residue
26.1
C in products in use
39.1
C in products in landfills
51.3
C stored in products & landfills
90.4
Net C removals related to managed forests
Methods and protocols for interagency collaborations in other regions.
288 +/- 15% Tg/yr
1 2000
1015 grams by eco-component
1 0000
Arboreal
Understory
Floor
Soil
8000
6000
4000
2000
52
62
19
19
19
70
19
77
19
87
19
92
20
00
20
10
20
20
20
30
20
40
20
50
0
Vist our web site:
http://www.fs.fed.us/ne/global/research/drb/drb.html
Predicting
Forest
Production,
Yield,
and N Leaching in the Chesapeake Basin
Predic ting forest
p roduction,
wate r yield and N leachingWater
losses in the Chesapea
ke Basin
The Chesapeake Bay is the largest estuary in the United States. Forest ecosystems in the Bay Basin are very sensitive and vulnerable to changes in environment because they
have already suffered a long history of human disturbances from development, population, pollution, and land-use changes. With concern of global change effects at regional
level, there are questions about how the changes (such as increase in N deposition) will affect forest productivity, health and water quality in the Bay Basin. An explicit way to
answer these questions is crucial to policy decisions managing ecosystems in the Basin. Process-based ecosystem models are indispensable tools for addressing these questions
because they are able to study spatial pattern of ecological processes and environmental factors that influence the m.
Other
Other Adjunct
Adjunct Studies
Studies
Check out the NE -FIA web site for more information
Futuring Wood Supply and Ecological Indicators
The PnE T model
The PnET-II model is a simple, lumped para met er, monthly-time-step model of carbon, nitrogen and water balances of forest. It can be used to estimate ca rbon storage, net
prima ry production (NPP), wood production, and water yield, as well as rates of N mine raliz ation, nitrification, N leaching losses and maximum N cycling in forest ecosystems.
The model has been applied to study forest ecosystems for contemporary cli mates and GCM project ed climate scena rios at both stand and regional levels. The model was well
validated for predictions of NPP, water yield and N losses in steams at numbers of location within the northeastern U.S. For regional studies of PnET, constant foliage Ns are
usually assigned to forest types. In this study, we applied foliage N equations in PnET. The equations are functions relating foliage N with climati c variables ac ross gradients.
The figures below display the results for
Predicte d An nual Water Yiel d (r uno ff)
Predicte d An nual Ne t Pr imary Prod uctio n
(g m -2 y r-1 )
(k g /ha /y r)
( cm )
( 2x N depos ition )
N Leaching los s to Strea m Wa ter
( 1x N depos ition )
(kg /ha /y r)
Visit our web site: www.nefa.conknet.com
• Cooperative Study: USDA NRS, NEFA States, NCASI, and Academia
• Two-year study funded by State & Private Forestry.
Figure 3b
Figure 3a
Figure 1
Figure 2
• Provided:
• Timberland area projections by habitat type,
• Wood-supply projections (inventory, growth, & removals),
• Quantif ied and projected new set of ecological indicators.
• Being used by States f or planning, policy development, and other needs.
National Forest System Assessment
NWOS
• Cooperative S tudy : USDA NRS - G reen & White NF’s.
• A ssessed demand for timber by primary wood processors.
• Compared demand to available timber supply (quality,
operability, and cost).
To be continued …
• Evaluated three treatments: regeneration cut, partial cut,
and diameter-limit cut.
• E stimated available timber supply for the NF Market Area.
• Results are being used in the NF plan revision process.
P4
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